diff --git a/examples/hello-world/hello-pt-resnet/fedavg_script_runner_pt.py b/examples/hello-world/hello-pt-resnet/fedavg_script_runner_pt.py
index a4955ed5a0..948d64d519 100644
--- a/examples/hello-world/hello-pt-resnet/fedavg_script_runner_pt.py
+++ b/examples/hello-world/hello-pt-resnet/fedavg_script_runner_pt.py
@@ -32,7 +32,8 @@
     # Add clients
     for i in range(n_clients):
         executor = ScriptRunner(
-            script=train_script, script_args=""  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
+            script=train_script,
+            script_args="",  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
         )
         job.to(executor, f"site-{i+1}")
 
diff --git a/examples/hello-world/hello-pt-resnet/src/hello-pt_cifar10_fl.py b/examples/hello-world/hello-pt-resnet/src/hello-pt_cifar10_fl.py
index 7395466c68..5dd0a49cc9 100644
--- a/examples/hello-world/hello-pt-resnet/src/hello-pt_cifar10_fl.py
+++ b/examples/hello-world/hello-pt-resnet/src/hello-pt_cifar10_fl.py
@@ -48,7 +48,10 @@ def main():
     client_name = sys_info["site_name"]
 
     train_dataset = CIFAR10(
-        root=os.path.join(DATASET_PATH, client_name), transform=transforms, download=True, train=True
+        root=os.path.join(DATASET_PATH, client_name),
+        transform=transforms,
+        download=True,
+        train=True,
     )
     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 
@@ -74,9 +77,19 @@ def main():
 
                 running_loss += cost.cpu().detach().numpy() / images.size()[0]
                 if i % 3000 == 0:
-                    print(f"Epoch: {epoch}/{epochs}, Iteration: {i}, Loss: {running_loss/3000}")
-                    global_step = input_model.current_round * steps + epoch * len(train_loader) + i
-                    summary_writer.add_scalar(tag="loss_for_each_batch", scalar=running_loss, global_step=global_step)
+                    print(
+                        f"Epoch: {epoch}/{epochs}, Iteration: {i}, Loss: {running_loss/3000}"
+                    )
+                    global_step = (
+                        input_model.current_round * steps
+                        + epoch * len(train_loader)
+                        + i
+                    )
+                    summary_writer.add_scalar(
+                        tag="loss_for_each_batch",
+                        scalar=running_loss,
+                        global_step=global_step,
+                    )
                     running_loss = 0.0
 
         print("Finished Training")